library(tidyverse)
library(readxl)
library(plotly)
Volley <- read_excel("/Users/hannahcope/DTS350ProjectData.xlsx")
volleyball <- Volley %>%
select(-...4, -...8) %>%
mutate('Game_SR' = `Game SR`) %>%
mutate('SR_Score' = `SR Score`) %>%
mutate('Kills_SR' = `Kill Off SR`) %>%
mutate('Game_Dig' = `Game Dig`) %>%
mutate('Dig_Score' = `Dig Score`) %>%
mutate('Kills_Dig' = `Kil off Dig`) %>%
mutate('Game_Conv' = `Came Conv`) %>%
mutate('Type' = `Type`) %>%
mutate('Converted' = `Converted?`) %>%
select(Game_SR, SR_Score, Kills_SR, Game_Dig, Dig_Score, Kills_Dig, Game_Conv, Type, Converted)
Game_levels <- c('vs. Las Vegas (Sectionals)', 'vs. Western Kentucky (Regionals)', 'vs. Purdue (Quarterfinals)', 'vs. Washington (Semifinals)', 'vs. Texas (Finals)')
SR_Games <- volleyball %>%
mutate(Game_SR = factor(Game_SR, levels = Game_levels)) %>%
select(Game_SR, SR_Score, Kills_SR) %>%
ggplot(aes(x = SR_Score, fill = Kills_SR)) +
geom_bar(position = 'dodge') +
facet_wrap(~Game_SR, nrow = 1) +
theme_bw() +
labs(x = "Serve Recieve Score",
y = "Total Swings",
title = "Kills Based on Serve Recieve Passing Scores by Game") +
scale_fill_discrete(name = "Kill?")
SR_Games
This plot tells me that when UK gets a perfect pass in serve receive, they almost always get a kill. Serve receive seems to be their strong suite, and where they get most of their points. This results in a good side out percentage. In the game against Purdue, Kentucky needed to convert more of their 3 passes into kills. The team serve received really well that game, but the passes were not used as they should be.
Dig_Games <- volleyball %>%
mutate(Game_Dig = factor(Game_Dig, levels = Game_levels)) %>%
select(Game_Dig, Dig_Score, Kills_Dig) %>%
drop_na(Game_Dig, Dig_Score, Kills_Dig) %>%
ggplot(aes(x = Dig_Score, fill = Kills_Dig)) +
geom_bar(position = 'dodge') +
facet_wrap(~Game_Dig, nrow = 1) +
theme_bw() +
labs(x = "Dig Score",
y = "Total Swings",
title = "Kills Based on Dig Scores by Game") +
scale_fill_discrete(name = "Kill?")
Dig_Games
This plot tells me that UK is not getting enough kills in transition. This is normal considering the ball is coming at the defenders a lot faster and is harder to control. I can assume that Washington and Texas got Kentucky in out of system situations a lot of times based on how many 1 passes they had, but Kentucky needs to be better at finding ways to turn those 1 passes into kills. I would suggest they work more on defending attacks so they are able to put their hitters in good positions in transition. This also tells me that UK most likely scores more of their points in serve receive or blocking.
Convert_Games <- volleyball %>%
mutate(Game_Conv = factor(Game_Conv, levels = Game_levels)) %>%
select(Game_Conv, Type, Converted) %>%
drop_na(Game_Conv, Type, Converted) %>%
ggplot(aes(x = Type, fill = Converted)) +
geom_bar(position = 'dodge') +
facet_wrap(~Game_Conv, nrow = 1) +
theme_bw() +
labs(x = "Type of Ball Given",
y = "Total Swings",
title = "Kills When Given a Freeball, Tip, or Out of System Swing by Game") +
scale_fill_discrete(name = "Converted?")
Convert_Games
This plot tells me that UK is not the best at defending tips and out of system attacks. I can assume that Texas was very good at placing their tips, finding the weak spots on the Kentucky defense with their tips since that was the game with the worst conversions for UK. Most likely, Kentucky was not able to get kills off of these balls because they were not able to get a good touch on defense. This is important because these are easy points for the opponent. On the other hand, UK does well at converting when given a freeball. This is the easiest ball a team can receive and it is important to take advantage of those balls.
SR <- volleyball %>%
select(SR_Score, Kills_SR) %>%
ggplot(aes(x = SR_Score, fill = Kills_SR)) +
geom_bar(position = 'dodge') +
theme_bw() +
labs(x = "Serve Recieve Score",
y = "Total Swings",
title = "Kills Based on Serve Recieve Passing Scores") +
scale_fill_discrete(name = "Kill?")
SR_plot <- ggplotly(SR)
SR_plot
Based no the percentages I was able to find becasue of this graph, it is obvious that Kentucky is more likely to get a kill off a perfect pass in serve receive. This makes sense. The better the pass, the easier it will be to convert into a kill. I suggest Kentucky works on their shots when the ball is not perfect.
Dig <- volleyball %>%
select(Dig_Score, Kills_Dig) %>%
drop_na(Dig_Score, Kills_Dig) %>%
ggplot(aes(x = Dig_Score, fill = Kills_Dig)) +
geom_bar(position = 'dodge') +
theme_bw() +
labs(x = "Dig Score",
y = "Total Swings",
title = "Kills Based on Dig Scores") +
scale_fill_discrete(name = "Kill?")
Dig_plot <- ggplotly(Dig)
Dig_plot
Once again, the numbers show that UK is more likely to score a kill off of a perfect dig. Kentucky needs to be able to produce more kills off of perfect passes. If they could out number the amount of balls that aren’t kills with balls that are kills off of perfect passes, they will be even more successful.
Convert <- volleyball %>%
select(Type, Converted) %>%
drop_na(Type, Converted) %>%
ggplot(aes(x = Type, fill = Converted)) +
geom_bar(position = 'dodge') +
theme_bw() +
labs(x = "Type of Ball Given",
y = "Total Swings",
title = "Kills When Given a Freeball, Tip, or Out of System Swing") +
scale_fill_discrete(name = "Converted?")
Convert_plot <- ggplotly(Convert)
Convert_plot
The numbers from this plot show that Kentucky converts freeballs 50% of the time, which is good. They need to be better about converting tips and out of system swings into kills. The plot shows that Kentucky needs to get better at converting on the easy things.